Metaverse Gait Authentication Dataset (MGAD)

1. Dataset Overview

The Metaverse Gait Authentication Dataset (MGAD) is a large-scale dataset for gait-based biometric authentication in virtual environments. It consists of gait data from 5,000 simulated users, generated using Unity 3D and processed using OpenPose and MediaPipe. This dataset is ideal for researchers working on biometric authentication, gait analysis, and AI-driven identity verification systems.

2. Data Structure & Format

• File Format: CSV
• Number of Samples: 5,000 users
• Number of Features: 16 gait-based features
• Columns: Each row represents a user with corresponding gait feature values
• Size: Approximately (mention size in MB/GB after upload)

3. Feature Descriptions

The dataset includes 16 extracted gait features:

• Stride Length (m): Average distance covered in one gait cycle.
• Step Frequency (steps/min): Number of steps taken per minute.
• Stance Phase Duration (s): Stance phase in a gait cycle.
• Swing Phase Duration (s): Duration of the swing phase in a gait cycle.
• Double Support Phase Duration (s): Time both feet are in contact with the ground.
• Step Length (m): Distance between consecutive foot placements.
• Cadence Variability (%): Variability in step rate.
• Hip Joint Angle (°): Maximum angle variation in the hip joint.
• Knee Joint Angle (°): Maximum flexion-extension knee angle.
• Ankle Joint Angle (°): Angle variation at the ankle joint.
• Avg. Vertical GRF (N): Average vertical ground reaction force.
• Avg. Anterior-Posterior GRF (N): Ground reaction force in the forward-backward direction.
• Avg. Medial-Lateral GRF (N): Ground reaction force in the side-to-side direction.
• Avg. COP Excursion (mm): Center of pressure movement during stance phase.
• Foot Clearance during Swing Phase (mm): Minimum height of the foot during the swing phase.
• Gait Symmetry Index (%): Measure of symmetry between left and right gait cycles.

4. How to Use the Dataset

• Load the dataset in Python using Pandas:

• Use the features for machine learning models in biometric authentication.
• Apply preprocessing techniques like normalization and feature scaling.
• Train and evaluate deep learning or ensemble models for gait recognition.

5. Citation & License

If you use this dataset, please cite it as follows:

Sandeep Ravikanti, "Metaverse Gait Authentication Dataset (MGAD)," IEEE DataPort, 2025. DOI: https://dx.doi.org/10.21227/rvh5-8842

6. Contact Information

For inquiries or collaborations, please contact: bitsrmit2023@gmail.com